Abstract
In this work, we introduce a method for enhancing distant supervision with state-change information for relation extraction. We provide a training dataset created via this process, along with manually annotated development and test sets. We present an analysis of the curation process and data, and compare it to standard distant supervision. We demonstrate that the addition of state-change information reduces noise when used for static relation extraction, and can also be used to train a relation-extraction system that detects a change of state in relations.- Anthology ID:
- 2022.lrec-1.597
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 5573–5579
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.597
- DOI:
- Cite (ACL):
- Jui Shah, Dongxu Zhang, Sam Brody, and Andrew McCallum. 2022. Enhanced Distant Supervision with State-Change Information for Relation Extraction. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 5573–5579, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Enhanced Distant Supervision with State-Change Information for Relation Extraction (Shah et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.lrec-1.597.pdf
- Code
- iesl/state-change-re